{"title":"眼成像的未来:功能性OCT与深度学习","authors":"Leopold Schmetterer","doi":"10.1111/aos.16812","DOIUrl":null,"url":null,"abstract":"<div>\n \n <section>\n \n <p>The future of ocular imaging lies at the intersection of two powerful technologies: Functional Optical Coherence Tomography (OCT) and Deep Learning. Functional OCT represents a leap forward in imaging technology by integrating traditional OCT with functional measurements like blood flow, tissue stiffness, tissue birefringence, oxygen metabolism and optoretinography. This comprehensive may offers deeper insights into ocular health and disease progression than conventional structural OCT. By capturing both structural and functional information, Functional OCT may our ability to detect and monitor conditions such as myopia, glaucoma, diabetic retinopathy, and age-related macular degeneration. Deep Learning, on the other hand, is a subset of artificial intelligence, analyzing the vast amounts of data generated by ocular imaging modalities. Through sophisticated algorithms, Deep Learning can identify intricate patterns and features in images that may elude human interpretation. This capability not only enhances diagnostic accuracy but also aids in automating image analysis, improving efficiency, and reducing the burden on healthcare professionals. The synergy between Functional OCT and Deep Learning holds immense promise for the future of ocular imaging. By integrating these technologies, we can achieve a comprehensive understanding of ocular physiology and pathology while enhancing diagnostic accuracy and efficiency. This convergence may lead to the development of advanced systems capable of real-time analysis and interpretation, facilitating point-of-care diagnostics and remote monitoring. However, realizing this potential requires addressing various challenges, including data privacy concerns, standardization of imaging protocols, and regulatory approval. Collaborative efforts between researchers, clinicians, and industry stakeholders are essential to overcoming these hurdles and harnessing the full benefits of Functional OCT and Deep Learning in ophthalmic practice.</p>\n </section>\n </div>","PeriodicalId":6915,"journal":{"name":"Acta Ophthalmologica","volume":"103 S284","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/10.1111/aos.16812","citationCount":"0","resultStr":"{\"title\":\"The future of ocular imaging: functional OCT versus deep learning\",\"authors\":\"Leopold Schmetterer\",\"doi\":\"10.1111/aos.16812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <section>\\n \\n <p>The future of ocular imaging lies at the intersection of two powerful technologies: Functional Optical Coherence Tomography (OCT) and Deep Learning. Functional OCT represents a leap forward in imaging technology by integrating traditional OCT with functional measurements like blood flow, tissue stiffness, tissue birefringence, oxygen metabolism and optoretinography. This comprehensive may offers deeper insights into ocular health and disease progression than conventional structural OCT. By capturing both structural and functional information, Functional OCT may our ability to detect and monitor conditions such as myopia, glaucoma, diabetic retinopathy, and age-related macular degeneration. Deep Learning, on the other hand, is a subset of artificial intelligence, analyzing the vast amounts of data generated by ocular imaging modalities. Through sophisticated algorithms, Deep Learning can identify intricate patterns and features in images that may elude human interpretation. This capability not only enhances diagnostic accuracy but also aids in automating image analysis, improving efficiency, and reducing the burden on healthcare professionals. The synergy between Functional OCT and Deep Learning holds immense promise for the future of ocular imaging. By integrating these technologies, we can achieve a comprehensive understanding of ocular physiology and pathology while enhancing diagnostic accuracy and efficiency. This convergence may lead to the development of advanced systems capable of real-time analysis and interpretation, facilitating point-of-care diagnostics and remote monitoring. However, realizing this potential requires addressing various challenges, including data privacy concerns, standardization of imaging protocols, and regulatory approval. Collaborative efforts between researchers, clinicians, and industry stakeholders are essential to overcoming these hurdles and harnessing the full benefits of Functional OCT and Deep Learning in ophthalmic practice.</p>\\n </section>\\n </div>\",\"PeriodicalId\":6915,\"journal\":{\"name\":\"Acta Ophthalmologica\",\"volume\":\"103 S284\",\"pages\":\"\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2025-01-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/10.1111/aos.16812\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Ophthalmologica\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/aos.16812\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"OPHTHALMOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Ophthalmologica","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/aos.16812","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPHTHALMOLOGY","Score":null,"Total":0}
The future of ocular imaging: functional OCT versus deep learning
The future of ocular imaging lies at the intersection of two powerful technologies: Functional Optical Coherence Tomography (OCT) and Deep Learning. Functional OCT represents a leap forward in imaging technology by integrating traditional OCT with functional measurements like blood flow, tissue stiffness, tissue birefringence, oxygen metabolism and optoretinography. This comprehensive may offers deeper insights into ocular health and disease progression than conventional structural OCT. By capturing both structural and functional information, Functional OCT may our ability to detect and monitor conditions such as myopia, glaucoma, diabetic retinopathy, and age-related macular degeneration. Deep Learning, on the other hand, is a subset of artificial intelligence, analyzing the vast amounts of data generated by ocular imaging modalities. Through sophisticated algorithms, Deep Learning can identify intricate patterns and features in images that may elude human interpretation. This capability not only enhances diagnostic accuracy but also aids in automating image analysis, improving efficiency, and reducing the burden on healthcare professionals. The synergy between Functional OCT and Deep Learning holds immense promise for the future of ocular imaging. By integrating these technologies, we can achieve a comprehensive understanding of ocular physiology and pathology while enhancing diagnostic accuracy and efficiency. This convergence may lead to the development of advanced systems capable of real-time analysis and interpretation, facilitating point-of-care diagnostics and remote monitoring. However, realizing this potential requires addressing various challenges, including data privacy concerns, standardization of imaging protocols, and regulatory approval. Collaborative efforts between researchers, clinicians, and industry stakeholders are essential to overcoming these hurdles and harnessing the full benefits of Functional OCT and Deep Learning in ophthalmic practice.
期刊介绍:
Acta Ophthalmologica is published on behalf of the Acta Ophthalmologica Scandinavica Foundation and is the official scientific publication of the following societies: The Danish Ophthalmological Society, The Finnish Ophthalmological Society, The Icelandic Ophthalmological Society, The Norwegian Ophthalmological Society and The Swedish Ophthalmological Society, and also the European Association for Vision and Eye Research (EVER).
Acta Ophthalmologica publishes clinical and experimental original articles, reviews, editorials, educational photo essays (Diagnosis and Therapy in Ophthalmology), case reports and case series, letters to the editor and doctoral theses.